Challenges and opportunities associated with rare-variant pharmacogenomics
Authors: Yitian Zhou, Roman Tremmel, Elke Schaeffeler, Matthias Schwab, Volker M. Lauschke
Journal: Trends in Pharmacological Sciences (2022)

Advances in sequencing technologies have facilitated the discovery of hundreds and thousands of human genetic variants, thus providing rich data resources to study the genetic complexity of pharmacogenes.Commonly used computational methods can be used to identify disease-causing variants; however, they underperform for variants that impact on drug response. So far, only a few algorithms for pharmacogenetic variant effect predictions have been developed. However, their predictive power is expected to be further improved by emerging massively parallel experimental assays and artificial intelligence-based methods.Combining pharmacogenomic data with extensive electronic medical records provides exciting opportunities for the identification of rare-variant associations.Significant gaps remain in the clinical implementation of functional rare genetic variants for patient care, even when considering polygenetic associations. Efforts from multiple parties including stakeholders from different healthcare areas are required. Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine. Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine. Pharmacological twin studies in which drug pharmacokinetics are compared between monozygotic and dizygotic twin pairs constitute important trial designs to assess the importance of heritable factors for drug disposition. Seminal studies indicated that 34–98% of interindividual differences in drug exposure are heritable, depending on the probe substrate [1.Lauschke V.M. Ingelman-Sundberg M. Prediction of drug response and adverse drug reactions: from twin studies to next generation sequencing.Eur. J. Pharm. Sci. 2019; 130: 65-77Crossref PubMed Scopus (36) Google Scholar]. However, genetic effects based on common polymorphisms can only explain part of this heritable variation, and a substantial fraction of the genetically encoded variability in drug pharmacokinetics remains to be elucidated [2.Matthaei J. et al.Heritability of metoprolol and torsemide pharmacokinetics.Clin. Pharmacol. Ther. 2015; 98: 611-621Crossref PubMed Scopus (48) Google Scholar]. Over the past few decades major advances in DNA sequencing technologies have revolutionized our understanding of the human genome. Since the sequencing and assembly of the first human genome, population-scale next-generation sequencing (NGS; see Glossary) projects have unveiled millions of genetic variants, including single-nucleotide variants (SNVs), small insertions and deletions (indels), as well as structural variants that can span up to multiple Mb. Aggregation of these sequencing data into consolidated publicly available variant repositories, such as the Genome Aggregation Database (gnomAD) [3.Karczewski K.J. et al.The mutational constraint spectrum quantified from variation in 141,456 humans.Nature. 2020; 581: 434-443Crossref PubMed Scopus (4070) Google Scholar], has provided comprehensive large-scale resources for pharmacogenomic studies. Recent analyses of such population-scale datasets have indicated that pharmacokinetic genes, which are commonly not disease-associated and lack endogenous substrates, contain a plethora of rare genetic variations with unknown functional consequences [4.Ingelman-Sundberg M. et al.Integrating rare genetic variants into pharmacogenetic drug response predictions.Hum. Genomics. 2018; 12: 26Crossref PubMed Scopus (127) Google Scholar, 5.Wright G.E.B. et al.The global spectrum of protein-coding pharmacogenomic diversity.Pharmacogenom. J. 2018; 18: 187-195Crossref PubMed Scopus (54) Google Scholar, 6.Ahn E. Park T. Analysis of population-specific pharmacogenomic variants using next-generation sequencing data.Sci. Rep. 2017; 7: 8416Crossref PubMed Scopus (18) Google Scholar]. 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Overcoming the gap between 'big data'-derived model predictions and real-world clinical outcomes arguably constitutes one of the greatest challenges of 21st century medical science and has been recognized by academics, pharmaceutical developers, political bodies, and regulators alike [13.Adamo J.E. et al.Translation of digital health technologies to advance precision medicine: informing regulatory science.Digit. Biomark. 2020; 4: 1-12Crossref PubMed Scopus (8) Google Scholar]. Specifically in pharmacogenomics, various innovative experimental and computational strategies have been presented to facilitate the translation of preclinical markers to clinical use. In the following sections we discuss pharmacogene-specific challenges associated with the identification of genetic variants as well as their functional interpretation and utilization for therapeutic decision-making (Figure 1). Furthermore, we highlight emerging experimental, computational, and clinical strategies for each step that promise to narrow the translational gap. NGS constitutes a suite of technologies in which the sequencing of short reads, typically between 50 and 500 bp in length, is massively parallelized with millions to billions of immobilized densely packed templates, which now allow the generation of >1 terabyte (TB) of sequence in a single day on one state-of-the-art instrument [14.Buermans H.P. den Dunnen J.T. Next generation sequencing technology: advances and applications.Biochim. Biophys. Acta. 2014; 1842: 1932-1941Crossref PubMed Scopus (496) Google Scholar]. The scope of sequencing approaches distinguishes between whole-exome sequencing (WES), whole-genome sequencing (WGS), and panel-based sequencing. WGS covers the entire human genome, including extensive intergenic or gene-free regions and intervals of unclear functional relevance. By focusing exclusively on exons, WES only interrogates ~2% of the human genome [15.Abecasis G.R. et al.A map of human genome variation from population-scale sequencing.Nature. 2010; 467: 1061-1073Crossref PubMed Scopus (6083) Google Scholar], thereby reducing sequencing breadth while still covering the majority of functionally important variations – which are predominantly found in coding regions. However, WES misses multiple important pharmacovariants in regulatory regions or introns, such as CYP2C19*17, CYP3A4*22, and CYP3A5*3. This incompleteness of genetic information hampers the prediction of drug metabolism-related phenotypes. Pharmacogenomic panel sequencing focuses specifically on pharmacogenetic loci, typically including exons, introns, and a few kb up- and downstream of the coding sequence (thus covering ~0.01–0.05% of the human genome [16.Daly A.K. Pharmacogenetics: a general review on progress to date.Br. Med. Bull. 2017; 124: 65-79PubMed Google Scholar]). Although panel-based sequencing offers the highest density of information when considering the number of pharmacogenetic variants covered per base sequenced, custom capture libraries are costlier, especially when only a few samples are to be sequenced. WGS, WES, and panel sequencing are all based on so-called short reads (i.e., individual fragments of continuous sequence that are only 100–600 bp in length), which are then mapped onto a reference genome. Particularly in pharmacogenetic loci with segmental duplications, variable numbers of tandem repeats, or nearby highly homologous pseudogenes, these short fragments are difficult to map unambiguously. Consequently, the accurate assignment of variants from short reads in these genes can be difficult. This poses severe limitations for multiple clinically relevant pharmacogenes, including CYP2D6 and CYP2B6, whose gene products metabolize >20% of commonly used drugs, as well as genes in the human leukocyte antigen (HLA) cluster which are strongly associated with severe adverse immune-mediated adverse events upon exposure to multiple drugs such as abacavir or carbamazepine [17.Lauschke V.M. et al.Pharmacogenomic biomarkers for improved drug therapy-recent progress and future developments.AAPS J. 2017; 20: 4Crossref PubMed Scopus (92) Google Scholar]. Although computational tools such as xHLA [18.Xie C. et al.Fast and accurate HLA typing from short-read next-generation sequence data with xHLA.Proc. Natl. Acad. Sci. U. S. A. 2017; 114: 8059-8064Crossref PubMed Scopus (84) Google Scholar] can attenuate these issues, they are locus-specific and have not yet found their way into widespread use. Recently introduced long-read sequencing methods that are capable of generating single reads ~100-fold longer than conventional short-read sequencing platforms promise to overcome these issues related to ambiguous mapping [19.Logsdon G.A. et al.Long-read human genome sequencing and its applications.Nat. Rev. Genet. 2020; 21: 597-614Crossref PubMed Scopus (322) Google Scholar]. Specifically, long-read sequencing results in continuous sequence fragments that typically exceed 10 kb (for comparison, the size of the CYP2D6 gene is 6.6 kb), thus allowing the entire sequence of a gene to be decode in a single read. In pharmacogenetics, long-read sequencing has been successfully used for the accurate identification of haplotype structures of complex pharmacogenetic loci such as CYP2D6 and HLA [20.Ammar R. et al.Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes.F1000Res. 2015; 4: 17Crossref PubMed Google Scholar,21.Liau Y. et al.Nanopore sequencing of the pharmacogene CYP2D6 allows simultaneous haplotyping and detection of duplications.Pharmacogenomics. 2019; 20: 1033-1047Crossref PubMed Scopus (25) Google Scholar]. The technical peculiarities of long-read sequencing mean that the established repertoire of short-read analytical tools is not applicable. Furthermore, owing to rapid developments in long-read sequencing in recent years, many workflows rely on customized scripts for quality control, error correction, read assembly, and haplotype calling that lack standardization and scalability [22.Amarasinghe S.L. et al.Opportunities and challenges in long-read sequencing data analysis.Genome Biol. 2020; 21: 30Crossref PubMed Scopus (548) Google Scholar]. However, as the throughput of long-read sequencing increases and costs per sequenced nucleotide decline, we envisage that technical issues related to variant identification will diminish in importance whereas challenges related to accurate variant interpretation and biomarker utilization will constitute the dominant barriers to clinical use of NGS in pharmacogenomics in the future. Epidemiological studies and experimental functional assays in recombinant cell lines have long constituted the main methods to characterize the functionality of pharmacogenetic variants [23.Zhou Y. et al.Computational methods for the pharmacogenetic interpretation of next generation sequencing data.Front. Pharmacol. 2018; 9: 1437Crossref PubMed Scopus (47) Google Scholar]. However, epidemiological approaches are not geared to systematically evaluating the landscape of pharmacogenetic variability because they are generally not powered to draw statistically sound conclusions about rare-variant effects [23.Zhou Y. et al.Computational methods for the pharmacogenetic interpretation of next generation sequencing data.Front. Pharmacol. 2018; 9: 1437Crossref PubMed Scopus (47) Google Scholar,24.Zhou Y. Lauschke V.M. Computational tools to assess the functional consequences of rare and noncoding pharmacogenetic variability.Clin. Pharmacol. 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To this end, a multitude of algorithms have been developed to infer variant deleteriousness based on sequence conservation, structural data, physicochemical properties of amino acid exchanges, and functional genomics information [23.Zhou Y. et al.Computational methods for the pharmacogenetic interpretation of next generation sequencing data.Front. Pharmacol. 2018; 9: 1437Crossref PubMed Scopus (47) Google Scholar]. Although these algorithms can statistically distinguish deleterious from neutral variations, multiple major limitations remain which hamper the application of most commonly used in silico models for pharmacogenetic predictions. First, most models perform reasonably well on training data, but their performance drops significantly when tested on independent variant sets – at least in part because of circularity in model development [25.Wang D. et al.A comparison on predicting functional impact of genomic variants.NAR Genom. Bioinform. 2022; 4: lqab122Crossref PubMed Scopus (4) Google Scholar,26.Grimm D.G. et al.The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity.Hum. Mutat. 2015; 36: 513-523Crossref PubMed Scopus (205) Google Scholar]. In addition, most algorithms are designed to predict variant pathogenicity rather than the functionality of the gene product per se. Their predictions are primarily based on sequence conservation, which is based on the assumption that variants in conserved regions are selected against and are thus more likely to be disease-causing. However, pharmacogenes are generally under low evolutionary pressure, and the functionality of pharmacogenetic variants is thus only poorly determined by conservation metrics [4.Ingelman-Sundberg M. et al.Integrating rare genetic variants into pharmacogenetic drug response predictions.Hum. Genomics. 2018; 12: 26Crossref PubMed Scopus (127) Google Scholar]. Related to this conceptual difference, most variant effect predictors use common variants identified in large-scale sequencing projects as training data for benign variants; however, many functionally relevant pharmacogenetic variations, such as rs1799853 (NG_008385.2:g.9133C>T defining the CYP2C9*2 allele), rs4244285 (NG_008384.3:g.24179G>A defining CYP2C19*2), and rs3892097 (NG_008376.4:g.6866G>A defining CYP2D6*4), are common in the general population and are thus included in the benign training set despite the fact that they clearly impact on gene function [27.Zhou Y. et al.Worldwide distribution of cytochrome P450 alleles: a meta-analysis of population-scale sequencing projects.Clin. Pharmacol. Ther. 2017; 102: 688-700Crossref PubMed Scopus (362) Google Scholar]. As a consequence, such algorithms perform poorly overall on pharmacogenetic variations. Specifically, methods based on purely evolutionary conservation showed the lowest predictive accuracy on pharmacogenomic variant sets, whereas functional prediction methods that also include additional features, such as physicochemical properties of amino acids, protein domain architectures, and functional genomics data, perform better overall [28.Zhou Y. et al.An optimized prediction framework to assess the functional impact of pharmacogenetic variants.Pharmacogenom. J. 2019; 19: 115-126Crossref PubMed Scopus (75) Google Scholar]. Algorithms that are specifically developed for analyses of pharmacogenetic variants are necessary to elucidate the functional contributions of novel and uncharacterized pharmacogenetic variants. As described above, the low throughput of conventional in vitro assays impedes the functional interpretation of the tens of thousands of rare pharmacogenetic variants identified in population-scale human sequencing data. To tackle this challenge, high-throughput methods have been developed that enable the study of sequence–function relationships across thousands of variants [29.Kinney J.B. McCandlish D.M. Massively parallel assays and quantitative sequence–function relationships.Annu. Rev. Genomics Hum. Genet. 2019; 20: 99-127Crossref PubMed Scopus (58) Google Scholar]. These include massively parallel reporter assays [30.van Arensbergen J. et al.High-throughput identification of human SNPs affecting regulatory element activity.Nat. Genet. 2019; 51: 1160-1169Crossref PubMed Scopus (104) Google Scholar] and deep mutational scanning [31.Fowler D.M. Fields S. Deep mutational scanning: a new style of protein science.Nat. Methods. 2014; 11: 801-807Crossref PubMed Scopus (557) Google Scholar] for the analysis of regulatory sequence variants and protein-coding mutations, respectively. In pharmacogenomics, deep mutational scanning has emerged as a promising methodology to experimentally interrogate the functional consequences of a large number of missense mutations in a gene of interest [32.Chiasson M. et al.Applying multiplex assays to understand variation in pharmacogenes.Clin. Pharmacol. Ther. 2019; 106: 290-294Crossref PubMed Scopus (9) Google Scholar,33.Lauschke V.M. Ingelman-Sundberg M. Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation.NPJ Genom. Med. 2020; 5: 9Crossref PubMed Scopus (30) Google Scholar]. Conceptually, deep mutational scanning is based on transfection of cells with a diverse library and subsequent evaluation of variant effects using a gene-specific assay that enables quantitative conclusions about protein activity or abundance [31.Fowler D.M. Fields S. Deep mutational scanning: a new style of protein science.Nat. Methods. 2014; 11: 801-807Crossref PubMed Scopus (557) Google Scholar]. Activity profiling has been successfully conducted for variants in the genes encoding the drug-metabolizing enzymes CYP2C9 and NUDT15, as well as in VKORC1 which encodes vitamin K epoxide reductase (VKOR) – an important determinant of warfarin response. For CYP2C9, direct readout of the activity of thousands of variant enzymes is provided by a multiplexed method based on a click chemistry-compatible CYP2C9-specific probe [34.Amorosi C.J. et al.Massively parallel characterization of CYP2C9 variant enzyme activity and abundance.Am. J. Hum. Genet. 2021; 108: 1735-1751Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar]. By contrast, NUDT15 variant profiling used an indirect toxicity-based readout. Specifically, cells expressing NUDT15 variants were exposed to 6-thioguanine and, leveraging that reduced functionality of NUDT15 sensitizes cells to thiopurines, the frequency of each variant before and after treatment was determined by NGS [35.Suiter C.C. et al.Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity.Proc. Natl. Acad. Sci. U. S. A. 2020; 117: 5394-5401Crossref PubMed Scopus (60) Google Scholar]. VKOR activity was profiled using a cell assay based on VKOR-dependent carboxylation of a cell-surface reporter protein, which is detected with a carboxylation-specific, fluorophore-labeled antibody [36.Chiasson M.A. et al.Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact.eLife. 2020; 9e58026Crossref PubMed Google Scholar]. Combined, these studies indicate that, although activity-based deep mutational scanning allows multiplexed functional profiling of variants on an unprecedented scale, the development of gene-specific assays (which cannot be easily transferred to other genes) remains a major hurdle. With this limitation in mind, more versatile assays based on multiplexed expression of variants as fusion constructs with a fluorescent reporter have been developed, which are based on the principle that increased degradation of the fusion construct due to misfolding of the variant protein results in lower levels of fluorescence [37.Zhang L. et al.CYP2C9 and CYP2C19: deep mutational scanning and functional characterization of genomic missense variants.Clin. Transl. Sci. 2020; 13: 727-742Crossref PubMed Scopus (20) Google Scholar,38.Matreyek K.A. et al.Multiplex assessment of protein variant abundance by massively parallel sequencing.Nat. Genet. 2018; 50: 874-882Crossref PubMed Scopus (191) Google Scholar]. The advantage of this approach is that the same experimental platform can be used for the characterization of different genes. However, correlation of variant abundance with functional activity is overall poor, and protein abundance only explained around half of the observed functional variation [34.Amorosi C.J. et al.Massively parallel characterization of CYP2C9 variant enzyme activity and abundance.Am. J. Hum. Genet. 2021; 108: 1735-1751Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar]. By now, assays based on functional or abundance-based deep mutational scanning principles have been applied to drug-metabolizing enzyme genes (CYP2C9, CYP2C19, TPMT, NUDT15), drug transporter genes (SLCO1B1), and other genes associated with drug response (VKORC1). The respective results have provided novel insights into variant-sensitive secondary structure elements and the molecular principles underlying the functional variability in these genes (Table 1). Furthermore, these extensively annotated variant sets promise to serve well as resources for training computational variant effect predictors that in turn can serve to extrapolate patterns from a subset of genes to the wider pharmacogenome. Similar approaches have already proved to be useful for the prediction of variant effects in disease-associated genes [39.Song H. et al.Inferring protein sequence–function relationships with large-scale positive-unlabeled learning.Cell Syst. 2021; 12: 92-101Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar,40.Gelman S. et al.Neural networks to learn protein sequence–function relationships from deep mutational scanning data.Proc. Natl. Acad. Sci. U. S. A. 2021; 118e2104878118Crossref Scopus (33) Google Scholar], and emerging strategies for the use of machine learning for pharmacogenetic applications are provided below.Table 1Deep mutational scanning studies for large-scale pharmacogenomic variant interrogationsPharmacogeneMethodaAbbreviation: FACS, fluorescence-activated cell sorting.Number of variantsKey findingsRefsCYP2C9Activity profiling based on the formation of metabolic adductsAbundance profiling based on a fluorescent protein reporter coupled to FACS and sequencing6370 abundance6142 activity2/3 of variants showed decreased functionProtein abundance accounted for half of the variation[34.Amorosi C.J. et al.Massively parallel characterization of CYP2C9 variant enzyme activity and abundance.Am. J. Hum. Genet. 2021; 108: 1735-1751Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar]CYP2C9Multiplexed generation of cells expressing GFP fusion proteins of pharmacogene variants coupled to FACS and sequencing to determine variant effects on protein levels109 abundance19/109 variants <25% expression[37.Zhang L. et al.CYP2C9 and CYP2C19: deep mutational scanning and functional characterization of genomic missense variants.Clin. Transl. Sci. 2020; 13: 727-742Crossref PubMed Scopus (20) Google Scholar]CYP2C19121 abundance36/121 variants <25% expression[37.Zhang L. et al.CYP2C9 and CYP2C19: deep mutational scanning and functional characterization of genomic missense variants.Clin. Transl. Sci. 2020; 13: 727-742Crossref PubMed Scopus (20) Google Scholar]SLCO1B1137 abundance6/137 variants <25% expression[91.Zhang L. et al.SLCO1B1: application and limitations of deep mutational scanning for genomic missense variant function.Drug Metab. Dispos. 2021; 49: 395-404Crossref PubMed Scopus (10) Google Scholar]VKORC1Activity profiling based on the formation of a VKOR activity-dependent cellular epitopeAbundance profiling based on a fluorescent protein reporter coupled to FACS and sequencing2695 abundance697 activityElucidation of VKOR structure; 25% of variants exhibit reduced expression or activity[36.Chiasson M.A. et al.Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact.eLife. 2020; 9e58026Crossref PubMed Google Scholar]TPMTAbundance profiling based on fluorescent protein reporter coupled to FACS and sequencing3689 abundanceAlteration of hydrophobic or long nonpolar aliphatic amino acids produced the largest decrease in protein abundance777 of 3689 variants resulted in reduced steady-state protein abundance[38.Matreyek K.A. et al.Multiplex assessment of protein variant abundance by massively parallel sequencing.Nat. Genet. 2018; 50: 874-882Crossref PubMed Scopus (191) Google Scholar]NUDT15Activity profiling based on toxicity assayAbundance profiling based on fluorescent protein reporter coupled to FACS and sequencing2922 abundance2935 activity1152 of 2922 variants have deleterious effects[35.Suiter C.C. et al.Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity.Proc. Natl. Acad. Sci. U. S. A. 2020; 117: 5394-5401Crossref PubMed Scopus (60) Google Scholar]a Abbreviation: FACS, fluorescence-activated cell sorting. Open table in a new tab AI provides exciting opportunities to improve the prediction of the functional consequences of rare variants. Machine learning is a subdiscipline of AI that identifies and extracts signal patterns from training data, and then transfers those 'learned' rules to make predictions in independent test sets. Machine learning is particularly suitable because it involves learning from examples rather than being programmed with a predetermined set of rules (Figure 2) [41.Rajkomar A. et al.Machine learning in medicine.N. Engl. J. Med. 2019; 380: 1347-1358Crossref PubMed Scopus (1314) Google Scholar]. In this 'learning' module, various strategies such as supervised or unsupervised approaches are applied [42.Libbrecht M.W. Noble W.S. Machine learning applications in genetics and genomics.Nat. Rev. Genet. 2015; 16: 321-332Crossref PubMed Scopus (1049) Google Scholar]. Supervised learning incorporates large numbers of features associated with a known phenotype – such as survival or drug response for each training sample – and consequently enables the prediction of these phenotypes for independent samples [43.Fröhlich H. et al.From hype to reality: data science enabling personalized medicine.BMC Med. 2018; 16: 150Crossref PubMed Scopus (211) Google Scholar]. By contrast, unsupervised learning identifies patterns from data commonly derived from omic technologies (e.g., gene expression) without previous annotation of phenotypic subclasses [44.Frazer J. et al.Disease variant prediction with deep generative models of evolutionary data.Nature. 2021; 599: 91-95Crossref PubMed Scopus (125) Google Scholar]. Thus, AI methods analyze and describe signals in a dataset of interest, and might thereby enable prediction of the effects of rare varia